1 ⭐ About Game of Thrones
Game of Thrones is an American fantasy drama television series created by David Benioff and D. B. Weiss for HBO. It is an adaptation of A Song of Ice and Fire, a series of fantasy novels by George R. R. Martin, the first of which is A Game of Thrones. It premiered on HBO in the United States on April 17, 2011, and concluded on May 19, 2019, with 73 episodes broadcast over eight seasons.
In this project we will utilize EDA to explore the Game of thrones dataset for:
Which season has the highest ratings?
Top 5 most viewed
Top 5 people who work with the most
Game of Thrones Dataset
colnames(Game_of_Thrones) <- c("season","no.ep","no.overall","title","time","directed","written","air_date","viewer","music", "cinematography", "editing", "imdb", "rotten", "metacritic", "ordered", "duration", "adepted", "synopsis")
Game_of_Thrones %>%
select(season, no.ep, title, directed, written, viewer, music, cinematography, editing, imdb, rotten, metacritic) %>%
datatable()1.1 Game of Thrones rating
We will see Which season has the highest ratings compare 3 web
rating_of_tree <- Game_of_Thrones %>%
select(imdb,
metacritic,
rotten) %>%
summarise(mean_imdb = round(mean(imdb),2),
mean_metacritic = round(mean(metacritic),2),
mean_rottrn = round(mean(rotten)/10,2))
colnames(rating_of_tree) <- c("IMDB","Metacritic","Rotten Tomatoes")
rating_of_tree## IMDB Metacritic Rotten Tomatoes
## 1 8.74 7.82 9.2rating_of_tree %>%
gather() %>%
ggplot(aes(key,
value,
fill=key,
label = gather(rating_of_tree)$key)) +
geom_col()+
geom_text(size=5,
position = position_dodge(0.9),
vjust=0)+
theme_minimal()+
theme(axis.text.x = element_blank(),
legend.position = "none")+
xlab("")+
ylab("")+
scale_fill_manual(values = c("#FFD700","#191970","#FF6347"))
1.1.1 IMDB ratings
in imdb website we will see season 4 have most rating
imdb_rating <- Game_of_Thrones %>%
select(season,
imdb) %>%
group_by(season) %>%
summarise(mean_imdb = round(mean(imdb),2))
imdb_rating %>%
pivot_longer(-season,
names_to = "variable",
values_to = "value") %>%
ggplot(aes(season, value))+
geom_point(size=5, colour = "#FFD700")+
geom_line(alpha=0.2,size=2,colour = "#FFD700")+
theme_minimal()+
theme(axis.text.x = element_text(angle = 0, hjust = 1, size = rel(1.5)),
axis.text.y = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, size = rel(2)))+
scale_x_continuous(breaks = 1:8)+
ggtitle("IMDB")+
xlab("")+
ylab("")
imdb_rating %>%
arrange(desc(mean_imdb))## # A tibble: 8 × 2
## season mean_imdb
## <int> <dbl>
## 1 4 9.23
## 2 7 9.03
## 3 6 8.99
## 4 1 8.97
## 5 3 8.93
## 6 2 8.81
## 7 5 8.71
## 8 8 6.421.1.2 Metacritic ratings
in Metacritic website we will see season 1 have most rating
rotten_rating <- Game_of_Thrones %>%
select(season,
metacritic) %>%
group_by(season) %>%
summarise(mean_metacritic = round(mean(metacritic),2))
rotten_rating %>%
pivot_longer(-season,
names_to = "variable",
values_to = "value") %>%
ggplot(aes(season, value))+
geom_point(size=5, colour = "#191970")+
geom_line(alpha=0.2,size=2,colour = "#191970")+
theme_minimal()+
theme(axis.text.x = element_text(angle = 0, hjust = 1, size = rel(1.5)),
axis.text.y = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, size = rel(2)))+
scale_x_continuous(breaks = 1:8)+
ggtitle("Metacritic")+
xlab("")+
ylab("")
rotten_rating %>%
arrange(desc(mean_metacritic))## # A tibble: 8 × 2
## season mean_metacritic
## <int> <dbl>
## 1 1 9.12
## 2 4 8.95
## 3 3 8.75
## 4 2 8.7
## 5 5 8.3
## 6 6 6.7
## 7 7 5.86
## 8 8 4.081.1.3 Rotten Tomato ratings
in Rotten Tomato website we will see season 1 have most rating
rotten_rating <- Game_of_Thrones %>%
select(season,
rotten) %>%
group_by(season) %>%
summarise(mean_rotten = round(mean(rotten),2))
rotten_rating %>%
pivot_longer(-season,
names_to = "variable",
values_to = "value") %>%
ggplot(aes(season, value))+
geom_point(size=5, colour = "#FF6347")+
geom_line(alpha=0.2,size=2,colour = "#FF6347")+
theme_minimal()+
theme(axis.text.x = element_text(angle = 0, hjust = 1, size = rel(1.5)),
axis.text.y = element_blank(),
legend.position = "none",
plot.title = element_text(hjust = 0.5, size = rel(2)))+
scale_x_continuous(breaks = 1:8)+
ggtitle("Rotten Tomoto")+
xlab("")+
ylab("")
rotten_rating %>%
arrange(desc(mean_rotten))## # A tibble: 8 × 2
## season mean_rotten
## <int> <dbl>
## 1 1 97.1
## 2 2 97
## 3 4 96.7
## 4 3 93.7
## 5 6 92.4
## 6 7 91.9
## 7 5 89.5
## 8 8 67.81.2 summarize compare 3 website top rating
IMDB is 4
Metacritic is 1
Rotten Tomoto is 1
season 1 win 2:1
season 1 overiew
Game_of_Thrones %>%
select(season, no.ep, title, directed, written, viewer, music, cinematography, editing) %>%
filter(season == 1) %>%
datatable()rating_of_season1 <- Game_of_Thrones %>%
filter(season == 1) %>%
select(no.ep,
imdb,
rotten,
metacritic) %>%
group_by(no.ep) %>%
summarise(mean_imdb = round(mean(imdb),2),
mean_rottrn = round(mean(rotten)/10,2),
mean_metacritic = round(mean(metacritic),2))
rating_of_season1## # A tibble: 10 × 4
## no.ep mean_imdb mean_rottrn mean_metacritic
## <int> <dbl> <dbl> <dbl>
## 1 1 8.9 10 9.1
## 2 2 8.6 10 8.9
## 3 3 8.5 8.1 8.7
## 4 4 8.6 10 9.1
## 5 5 9 9.5 9
## 6 6 9.1 10 9.2
## 7 7 9.1 10 9.4
## 8 8 8.9 9.5 8.9
## 9 9 9.6 10 9.5
## 10 10 9.4 10 9.41.2.1 IMDB ratings
ep 9 is most rating 9.6 score
rating_of_season1 %>%
select(no.ep, mean_imdb) %>%
pivot_longer(-no.ep,
names_to = "variable",
values_to = "value")%>%
ggplot(aes(no.ep,
value,
colour= variable))+
geom_point(size=4, colour = "#FFD700")+
geom_line(alpha=0.3,size=2, colour = "#FFD700")+
theme_minimal()+
labs(title = "Ralationship between rating and episode in season 1",
x = "episode",
y= "rating")+
scale_x_continuous(breaks = 1:10)
1.2.2 Metacritic ratings
ep 9 is most rating 9.5 score
rating_of_season1 %>%
select(no.ep, mean_metacritic) %>%
pivot_longer(-no.ep,
names_to = "variable",
values_to = "value")%>%
ggplot(aes(no.ep,
value,
colour= variable))+
geom_point(size=4, colour = "#191970")+
geom_line(alpha=0.3,size=2, colour = "#191970")+
theme_minimal()+
labs(title = "Ralationship between rating and episode in season 1",
x = "episode",
y= "rating")+
scale_x_continuous(breaks = 1:10)
1.2.3 Rotten Tomato ratings
ep 1,2,4,6,7,9,10 😂 is most rating 100 score
rating_of_season1 %>%
select(no.ep, mean_rottrn) %>%
pivot_longer(-no.ep,
names_to = "variable",
values_to = "value")%>%
ggplot(aes(no.ep,
value,
colour= variable))+
geom_point(size=4, colour = "#FF6347")+
geom_line(alpha=0.3,size=2, colour = "#FF6347")+
theme_minimal()+
labs(title = "Ralationship between rating and episode in season 1",
x = "episode",
y= "rating")+
scale_x_continuous(breaks = 1:10)
Whattttttttt!!!!!!!!!!!
1.3 Who is director and wirttor
In season 1 ep. 9
directed by Alan Taylor
written by David Benioff, D. B. Weiss
cinematography by Alik Sakharov
editing by Frances Parker
Game_of_Thrones %>%
select(season, no.ep, title, directed, written, viewer, music, cinematography, editing) %>%
filter(season == 1, no.ep == 9) %>%
datatable()2 😎 Top 5 most viewed
We will plot a simple bar plot with No of U.S. Viewers of the Episode in Millions
top_5_viewed <- Game_of_Thrones %>%
select(1,2,4,9) %>%
arrange(desc(viewer)) %>%
head(5)top_5_viewed %>%
ggplot(aes(title,
viewer,
label = title,
fill=title))+
geom_col()+
geom_text(size=3,
position = position_dodge(0.9),
vjust=0)+
theme_minimal()+
labs(title = "Top 5 most viewed people",
x = "episode",
y= "view")+
theme(axis.text.x = element_blank(),
legend.position = "none")
The Iron Throne WIN!!!
Game_of_Thrones %>%
filter(title == "The Iron Throne") %>%
select(synopsis)%>%
datatable()2.1 Who is director and wirttor
In season 8 ep. 6 The Iron Throne
directed by David Benioff & D. B. Weiss
written by David Benioff, D. B. Weiss
cinematography by Jonathan Freeman
editing by Katie Weiland
Game_of_Thrones %>%
select(season, no.ep, title, directed, written, viewer, music, cinematography, editing) %>%
filter(title == "The Iron Throne") %>%
datatable()3 🤩 Top 5 people who work with the most
In Game of Throne dataset we have position director, writtor, cinematographer, editor let see top 5 people who work with the most
3.1 director
diredtors <- Game_of_Thrones %>%
select(directed) %>%
group_by(directed) %>%
count() %>%
arrange(desc(n)) %>%
filter(n >= 5) %>%
head(5)
colnames(diredtors) <- c("directed", "amount")
datatable(diredtors)diredtors %>%
ggplot(aes(directed,
amount,
label = diredtors$directed,
fill=directed))+
geom_col()+
geom_text(size=3,
position = position_dodge(0.9),
vjust=0)+
theme_minimal()+
labs(title = "Top 5 directors who have worked with the most",
x = "director",
y= "amount")+
theme(axis.text.x = element_blank(),
legend.position = "none")
The most directing is David Nutter
let’s see what episode directed by David Nutter
Game_of_Thrones %>%
select(season, no.ep, title, viewer, directed) %>%
filter(directed == "David Nutter") %>%
datatable()3.2 writtor
writtor <- Game_of_Thrones %>%
select(written) %>%
group_by(written) %>%
count() %>%
arrange(desc(n)) %>%
head(5)
colnames(writtor) <- c("writter", "amount")
datatable(writtor)writtor %>%
ggplot(aes(writter,
amount,
label = writtor$writter,
fill=writter))+
geom_col()+
geom_text(size=3,
position = position_dodge(0.9),
vjust=0)+
theme_minimal()+
labs(title = "Top 5 writtor who have worked with the most",
x = "writtor",
y= "amount")+
theme(axis.text.x = element_blank(),
legend.position = "none")
The most writer is David Benioff, D. B. Weiss
let’s see what episode written by David Benioff, D. B. Weiss
Game_of_Thrones %>%
select(season, no.ep, title, viewer, written) %>%
filter(written == "David Benioff, D. B. Weiss") %>%
datatable()3.3 cinematographer
cinematographer <- Game_of_Thrones %>%
select(cinematography) %>%
group_by(cinematography) %>%
count() %>%
arrange(desc(n)) %>%
head(5)
colnames(cinematographer) <- c("cinematography", "amount")
datatable(cinematographer)cinematographer %>%
ggplot(aes(cinematography,
amount,
label = cinematographer$cinematography,
fill=cinematography))+
geom_col()+
geom_text(size=3,
position = position_dodge(0.9),
vjust=0)+
theme_minimal()+
labs(title = "Top 5 writtor who have worked with the most",
x = "writtor",
y= "amount")+
theme(axis.text.x = element_blank(),
legend.position = "none")
The most cinematographer is Anette Haellmigk
let’s see what episode cinematography by Anette Haellmigk
Game_of_Thrones %>%
select(season, no.ep, title, viewer, cinematography) %>%
filter(cinematography == "Anette Haellmigk") %>%
datatable()3.4 editor
editor <- Game_of_Thrones %>%
select(editing) %>%
group_by(editing) %>%
count() %>%
arrange(desc(n)) %>%
head(5)
colnames(editor) <- c("editing", "amount")
datatable(editor)editor %>%
ggplot(aes(editing,
amount,
label = editor$editing,
fill=editing))+
geom_col()+
geom_text(size=3,
position = position_dodge(0.9),
vjust=0)+
theme_minimal()+
labs(title = "Top 5 writtor who have worked with the most",
x = "writtor",
y= "amount")+
theme(axis.text.x = element_blank(),
legend.position = "none")
The most editing is Katie Weiland
let’s see what episodeediting by Katie Weiland
Game_of_Thrones %>%
select(season, no.ep, title, viewer, editing) %>%
filter(editing == "Katie Weiland") %>%
datatable()4 🚩 Summary
This notebook showed that Game of Throne season 1 ep.9 Baelor is most rating directedAlan Taylor by and writter byDavid Benioff, D. B. Weiss most of viewer is The Iron Throne directed and writer byDavid Benioff & D. B. Weiss Next time when I want to watch cinematic , I will try the cinematic directed and writer by David Benioff, D. B. Weiss
5 🪑 Reference:
[1] https://www.kaggle.com/datasets/iamsouravbanerjee/game-of-thrones-dataset
Thanks for reading !
